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Related papers: Motion Anything: Any to Motion Generation

200 papers

The field has made significant progress in synthesizing realistic human motion driven by various modalities. Yet, the need for different methods to animate various body parts according to different control signals limits the scalability of…

Computer Vision and Pattern Recognition · Computer Science 2023-11-29 Zixiang Zhou , Yu Wan , Baoyuan Wang

Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Ziye Li , Hao Luo , Xincheng Shuai , Henghui Ding

Generating 3D human motions from text is a challenging yet valuable task. The key aspects of this task are ensuring text-motion consistency and achieving generation diversity. Although recent advancements have enabled the generation of…

Computer Vision and Pattern Recognition · Computer Science 2025-08-29 Zheng Qin , Yabing Wang , Minghui Yang , Sanping Zhou , Ming Yang , Le Wang

We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows…

Computer Vision and Pattern Recognition · Computer Science 2022-07-19 Qiujing Lu , Yipeng Zhang , Mingjian Lu , Vwani Roychowdhury

Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by…

Computer Vision and Pattern Recognition · Computer Science 2026-05-22 Lee Hsin-Ying , Hanwen Jiang , Yiqun Mei , Jing Shi , Ming-Hsuan Yang , Zhixin Shu

Human motion synthesis is an important task in computer graphics and computer vision. While focusing on various conditioning signals such as text, action class, or audio to guide the generation process, most existing methods utilize…

Computer Vision and Pattern Recognition · Computer Science 2024-05-14 Kebing Xue , Hyewon Seo

Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Karran Pandey , Matheus Gadelha , Yannick Hold-Geoffroy , Karan Singh , Niloy J. Mitra , Paul Guerrero

Human motion generation aims to produce plausible human motion sequences according to various conditional inputs, such as text or audio. Despite the feasibility of existing methods in generating motion based on short prompts and simple…

Multimedia · Computer Science 2024-11-12 Bo Han , Hao Peng , Minjing Dong , Yi Ren , Yixuan Shen , Chang Xu

Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured…

Computer Vision and Pattern Recognition · Computer Science 2024-01-25 Nhat M. Hoang , Kehong Gong , Chuan Guo , Michael Bi Mi

Human motion generation, a cornerstone technique in animation and video production, has widespread applications in various tasks like text-to-motion and music-to-dance. Previous works focus on developing specialist models tailored for each…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Mingyuan Zhang , Daisheng Jin , Chenyang Gu , Fangzhou Hong , Zhongang Cai , Jingfang Huang , Chongzhi Zhang , Xinying Guo , Lei Yang , Ying He , Ziwei Liu

Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory desired by…

Computer Vision and Pattern Recognition · Computer Science 2022-03-08 Ligong Han , Jian Ren , Hsin-Ying Lee , Francesco Barbieri , Kyle Olszewski , Shervin Minaee , Dimitris Metaxas , Sergey Tulyakov

Existing text-driven motion generation methods often treat synthesis as a bidirectional mapping between language and motion, but remain limited in capturing the causal logic of action execution and the human intentions that drive behavior.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-06 Junyu Shi , Yong Sun , Zhiyuan Zhang , Lijiang Liu , Zhengjie Zhang , Yuxin He , Qiang Nie

Text to Motion aims to generate human motions from texts. Existing settings rely on limited Action Texts that include action labels, which limits flexibility and practicability in scenarios difficult to describe directly. This paper extends…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Runqi Wang , Caoyuan Ma , Guopeng Li , Hanrui Xu , Yuke Li , Zheng Wang

Image animation has become a promising area in multimodal research, with a focus on generating videos from reference images. While prior work has largely emphasized generic video generation guided by text, music-driven dance video…

Computer Vision and Pattern Recognition · Computer Science 2025-02-03 Zhikang Dong , Weituo Hao , Ju-Chiang Wang , Peng Zhang , Pawel Polak

End-to-end human animation, such as audio-driven talking human generation, has undergone notable advancements in the recent few years. However, existing methods still struggle to scale up as large general video generation models, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Gaojie Lin , Jianwen Jiang , Jiaqi Yang , Zerong Zheng , Chao Liang

Large language models (LLMs) have unified diverse linguistic tasks within a single framework, yet such unification remains unexplored in human motion generation. Existing methods are confined to isolated tasks, limiting flexibility for…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Wendong Bu , Kaihang Pan , Yuze Lin , Jiacheng Li , Kai Shen , Wenqiao Zhang , Juncheng Li , Jun Xiao , Siliang Tang

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations.…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Guojun Lei , Chi Wang , Hong Li , Rong Zhang , Yikai Wang , Weiwei Xu

Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, most rely on fixed skeletal templates, which prevent them from…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Keyi Chen , Mingze Sun , Zhenyu Liu , Zhangquan Chen , Ruqi Huang

Attributes such as style, fine-grained text, and trajectory are specific conditions for describing motion. However, existing methods often lack precise user control over motion attributes and suffer from limited generalizability to unseen…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 Mingjie Wei , Xuemei Xie , Guangming Shi

Character animation in real-world scenarios necessitates a variety of constraints, such as trajectories, key-frames, interactions, etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control…

Computer Vision and Pattern Recognition · Computer Science 2024-05-30 Hanchao Liu , Xiaohang Zhan , Shaoli Huang , Tai-Jiang Mu , Ying Shan